Construction of new hydrophobic δ-MnO<sub>2</sub> flower-like/kaolin/(3-aminopropyl) triethoxysilane/metal mesh membrane for oil/water separation: modeling of fouling process
Bibliographic record
Abstract
A composite material consisting of δ-MnO2 flower-like/kaolin was modified with (3-aminopropyl) triethoxysilane (APTES) and utilized for separating sunflower oil from water using a metal mesh membrane. The δ-MnO2 flower-like/kaolin/APTES/metal mesh membrane was characterized using various techniques such as X-ray diffraction analysis, Fourier-transform infrared spectroscopy, scanning electron microscopy, energy dispersive spectroscopy mapping, Brunauer–Emmett–Teller theory/Barrett–Joyner–Halenda, atomic force microscopy, and contact angle. Based on the outcomes of the conducted experiments, it was observed that the maximum porosity was associated with the δ-MnO2 flower-like/kaolin/APTES 15 (wt.%)/metal mesh (MKA(15 wt.%)M) membrane, and the porosity was calculated to be 0.58%. The blocking filtration model was employed, and a complete blocking model was obtained with a value of n = 2.15 for MKA(15 wt.%)M membrane. The response surface methodology based on Box–Behnken design was utilized to investigate the effect of parameters such as weight percentage of APTES (wt.%), mass of δ-MnO2 flower-like/kaolin/APTES (MKA) (g), volume of oil in water (mL), and temperature (°C) on the flux (L/m2·h) and rejection (%). Under optimal conditions, weight percentage of 13.32 (wt.%) of APTES, concentration of 0.2 (g) of MKA, volume of 37.10 (mL) of oil in water, and a temperature of 59.06 (°C) were obtained. These conditions resulted in a flux of 5977 (L/m2·h) and a rejection rate of 99.99% for 9 s. The kinetic studies indicated that the pseudo-second-order model had the highest correlation coefficient of 0.9991, thus displaying the most agreement with the experimental data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".